In processing of digital media streams (e.g., digital audio streams), it is found that there is a demand for two types of algorithms: 1) Block-Based (BB) algorithms, where blocks of audio data are accumulated and then processed as a unit, and 2) Sample-by-Sample (SS) algorithms, which process audio data a sample at a time. Frequency domain or BB algorithms have the advantage of greater computational flexibility, e.g., longer Finite Impulse Response or FIR filters may be realizable, and Fast Fourier Transform or FFT based algorithms are enabled, with a trade-off that blocks of data must be accumulated before processing can begin, thus adding latency. SS processing affords the advantages of very low latency and overhead, and substantially instantaneous response to changes in parameters, which can be very beneficial in applications such as, for example, changing the parameters to a graphic equalizer. The low overhead of SS processing greatly simplifies the dynamic loading and unloading of different SS software algorithms in an audio system, which enables quick modification to the effects processing of an audio stream in response to real-time input.
Prior art Digital Signal Processing or DSP systems are designed to exclusively perform either as BB or SS processors, but not both simultaneously. Accordingly, the standard prior art approach has been to either process the SS and BB algorithms in separate engines, or to convert the SS algorithms to BB algorithms and tolerate a latency and overhead penalty to the performance of the SS algorithms.